Changes in an operating parameter of a network system can impact network performance with respect to another operating parameter of the network system. Systems designs attempt to provide tools to allow the systems to predict system behavior when the system detects a network change. Current prediction tools are based on predictive system models, which are models of system variables and relationships among various variables.
Predictive models can traditionally be classified as white-box or black-box models. White-box modeling models changes based on known data and known system configurations. Thus, white-box modeling predicts behavior for known circumstances. Black-box modeling allows for interpolation, which is prediction not based on prior observation, but instead based on training data. The training data is data that estimates what might happen if a variable in system operation changes. Both types of modeling predict system behavior when a change is introduced into the system. Both types of modeling can be used to proactively assess system behavior prior to making the change that is assessed by the modeling.
Increasingly, white-box modeling is becoming more difficult and time consuming due to highly complex system configurations and the complex interplay between the various hardware and software components in network systems. An example of a network system that is increasing in complexity is a network storage system, which can experience significant interaction between hardware and software components under different workload conditions.
Similarly, black-box modeling is becoming increasingly difficult and time consuming. Black-box models are only good for known configurations and system configuration/operation combinations for which they have been trained. Performing accurate a priori training for black-box models is impractical for the combinatorially expansive number of hardware and software combinations of current network systems.
Additionally, the dynamic runtime nature of system configurations and system operating conditions negatively impacts the ability to model the system. Even if the possible combinations of system configurations can be foreseen, there may not be a practical way to model all the combinations in a real system with limited computational resources. The combinations of system configurations can lead to configurations that are impractical to accurately model with any degree of confidence, which makes it impractical to train the system for black-box modeling.